3 papers in the archive (2024–2026), average empirical rigor 3.2/10, average math complexity 8.9/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Risk Management & Tail Risk, Options & Derivatives Pricing, Volatility Modeling & Forecasting.
Frequent co-authors: Tahir Choulli (3), Michèle Vanmaele (1), Mich`ele Vanmaele (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
This paper explores the application and significance of the second-order Esscher pricing model in option pricing and risk management. We split the study into two main parts. First, we focus on the constant jump diffusion (CJD) case, analyzing the behavior of option prices as a function of the second
This paper considers the setting constituted by the triplet $(S,\mathbb{F},τ)$. The pair $(S,\mathbb{F})$ is the initial model where $S$ is the discounted price process of risky assets (Stocks) and $\mathbb{F}$ is the ``public" flow of information that is available to all agents over time. $τ$ is an
In this paper, we introduce the second-order Esscher pricing notion for continuous-time models. Depending whether the stock price $S$ or its logarithm is the main driving noise/shock in the Esscher definition, we obtained two classes of second-order Esscher densities called linear class and exponent